Lupus data simulation DE results
Celltype: combined
Milan Malfait
May 30, 2022
library(here)
library(tidyverse)
library(cowplot)
library(GGally)
theme_set(theme_light(base_size = 14))
library(scuttle)
library(scater)
library(scran)
library(kableExtra)
library(SCandwichCompanion)
library(iCOBRA)Setup
## Directory setup
here_root <- "benchmarks/lupus"
here::i_am(file.path(here_root, "analysis/lupus-sim-results.Rmd"))
#> here() starts at /Users/milan/Projects/SCandwich-analyses
data_dir <- here::here(here_root, "data")
stopifnot(dir.exists(data_dir))
res_dir <- here::here(here_root, "results")
stopifnot(dir.exists(res_dir))- Using
methods: muscat, sandwichTtestLRPrior1, pseudobulkSandwichTtest, NBpseudobulkSandwichTtest - Using
celltype: combined - Using
prop_DE: 0.05
Load results
res_files <- map(prop_DE, ~ get_sim_res_files(
dataset = "lupus", methods = methods,
prop_DE = .x, celltype = celltype
)) %>% set_names(paste0("prop_DE_", prop_DE))
res_list <- map_depth(res_files, 2, readRDS)
## Check for expected data structure
stopifnot(unlist(map_depth(res_list, 4, ~ is.data.frame(.x$results))))Load SCE objects
sce_files <- map(prop_DE, ~ get_SCE_files(
dataset = "lupus",
which = "sim_replicates", prop_DE = .x,
celltype = celltype
)) %>% set_names(paste0("prop_DE_", prop_DE))
sce_objects <- lapply(sce_files, readRDS)Data overview
- The results were generated from 1 simulation replicates
- Each replicate was generated by randomly splitting the subjects in two mock groups
- The same mock replicates were used for each
prop_DElevel. I.e.prop_DE_0.1$replicate1andprop_DE_0.05$replicate1will have the same mock group assignment - DE was introduced by swapping genes in one of the mock groups
- Cells per subject were sub-sampled to 100 cells per subject
map_dfr(sce_objects,
~ map_dfr(.x, function(x) c(nrows = nrow(x), ncols = ncol(x)),
.id = "replicate"
),
.id = "prop_DE"
)Subjects are divided across mock groups as follows:
lapply(sce_objects[[1]], function(x) table(x$ind_cov, x$mock_group))
#> $replicate_1
#>
#> A B
#> IGTB141_IGTB141 964 0
#> IGTB143_IGTB143 1417 0
#> IGTB195_IGTB195 0 1571
#> IGTB469_IGTB469 1263 0
#> IGTB498_IGTB498 1594 0
#> IGTB508_IGTB508 0 1317
#> IGTB514_IGTB514 1333 0
#> IGTB645_IGTB645 0 1299
#> IGTB670_IGTB670 1290 0
#> IGTB826_IGTB826 0 1154
#> IGTB884_IGTB884 0 967
#> IGTB986_IGTB986 926 0
#> IGTB1372_IGTB1372 0 836
#> IGTB1506_IGTB1506 0 927
#> IGTB1539_IGTB1539 0 818
#> IGTB1575_IGTB1575 823 0
#> IGTB1650_IGTB1650 1238 0
#> IGTB1762_IGTB1762 1815 0
#> IGTB1768_IGTB1768 1545 0
#> IGTB1789_IGTB1789 0 1105
#> IGTB1793_IGTB1793 1233 0
#> IGTB1814_IGTB1814 0 1400
#> IGTB1815_IGTB1815 1063 0
#> IGTB1826_IGTB1826 0 974
#> IGTB1827_IGTB1827 1117 0
#> IGTB1828_IGTB1828 959 0
#> IGTB1840_IGTB1840 1422 0
#> IGTB1844_IGTB1844 1252 0
#> IGTB1871_IGTB1871 0 973
#> IGTB1895_IGTB1895 0 1098
#> IGTB1901_IGTB1901 0 960
#> IGTB1906_IGTB1906 779 0
#> IGTB1908_IGTB1908 0 773
#> IGTB1913_IGTB1913 0 1659
#> IGTB1916_IGTB1916 0 985
#> IGTB1921_IGTB1921 1468 0
#> IGTB1952_IGTB1952 0 1263
#> IGTB1966_IGTB1966 0 1782
#> IGTB1974_IGTB1974 957 0
#> IGTB1982_IGTB1982 0 827
#> IGTB1987_IGTB1987 0 1526
#> IGTB1996_IGTB1996 0 622
#> IGTB2007_IGTB2007 1001 0
#> IGTB2065_IGTB2065 954 0
#>
#> $replicate_2
#>
#> A B
#> IGTB141_IGTB141 0 964
#> IGTB143_IGTB143 1417 0
#> IGTB195_IGTB195 1571 0
#> IGTB469_IGTB469 0 1263
#> IGTB498_IGTB498 1594 0
#> IGTB508_IGTB508 1317 0
#> IGTB514_IGTB514 0 1333
#> IGTB645_IGTB645 0 1299
#> IGTB670_IGTB670 0 1290
#> IGTB826_IGTB826 1154 0
#> IGTB884_IGTB884 967 0
#> IGTB986_IGTB986 0 926
#> IGTB1372_IGTB1372 0 836
#> IGTB1506_IGTB1506 0 927
#> IGTB1539_IGTB1539 818 0
#> IGTB1575_IGTB1575 0 823
#> IGTB1650_IGTB1650 0 1238
#> IGTB1762_IGTB1762 1815 0
#> IGTB1768_IGTB1768 1545 0
#> IGTB1789_IGTB1789 0 1105
#> IGTB1793_IGTB1793 0 1233
#> IGTB1814_IGTB1814 0 1400
#> IGTB1815_IGTB1815 1063 0
#> IGTB1826_IGTB1826 974 0
#> IGTB1827_IGTB1827 1117 0
#> IGTB1828_IGTB1828 0 959
#> IGTB1840_IGTB1840 1422 0
#> IGTB1844_IGTB1844 1252 0
#> IGTB1871_IGTB1871 0 973
#> IGTB1895_IGTB1895 1098 0
#> IGTB1901_IGTB1901 960 0
#> IGTB1906_IGTB1906 779 0
#> IGTB1908_IGTB1908 773 0
#> IGTB1913_IGTB1913 0 1659
#> IGTB1916_IGTB1916 0 985
#> IGTB1921_IGTB1921 1468 0
#> IGTB1952_IGTB1952 0 1263
#> IGTB1966_IGTB1966 1782 0
#> IGTB1974_IGTB1974 0 957
#> IGTB1982_IGTB1982 827 0
#> IGTB1987_IGTB1987 0 1526
#> IGTB1996_IGTB1996 0 622
#> IGTB2007_IGTB2007 0 1001
#> IGTB2065_IGTB2065 954 0
#>
#> $replicate_3
#>
#> A B
#> IGTB141_IGTB141 0 964
#> IGTB143_IGTB143 0 1417
#> IGTB195_IGTB195 1571 0
#> IGTB469_IGTB469 0 1263
#> IGTB498_IGTB498 1594 0
#> IGTB508_IGTB508 1317 0
#> IGTB514_IGTB514 0 1333
#> IGTB645_IGTB645 1299 0
#> IGTB670_IGTB670 0 1290
#> IGTB826_IGTB826 1154 0
#> IGTB884_IGTB884 967 0
#> IGTB986_IGTB986 926 0
#> IGTB1372_IGTB1372 836 0
#> IGTB1506_IGTB1506 927 0
#> IGTB1539_IGTB1539 0 818
#> IGTB1575_IGTB1575 0 823
#> IGTB1650_IGTB1650 0 1238
#> IGTB1762_IGTB1762 0 1815
#> IGTB1768_IGTB1768 1545 0
#> IGTB1789_IGTB1789 1105 0
#> IGTB1793_IGTB1793 1233 0
#> IGTB1814_IGTB1814 0 1400
#> IGTB1815_IGTB1815 1063 0
#> IGTB1826_IGTB1826 0 974
#> IGTB1827_IGTB1827 0 1117
#> IGTB1828_IGTB1828 0 959
#> IGTB1840_IGTB1840 1422 0
#> IGTB1844_IGTB1844 0 1252
#> IGTB1871_IGTB1871 973 0
#> IGTB1895_IGTB1895 0 1098
#> IGTB1901_IGTB1901 0 960
#> IGTB1906_IGTB1906 779 0
#> IGTB1908_IGTB1908 0 773
#> IGTB1913_IGTB1913 1659 0
#> IGTB1916_IGTB1916 985 0
#> IGTB1921_IGTB1921 1468 0
#> IGTB1952_IGTB1952 1263 0
#> IGTB1966_IGTB1966 0 1782
#> IGTB1974_IGTB1974 957 0
#> IGTB1982_IGTB1982 0 827
#> IGTB1987_IGTB1987 0 1526
#> IGTB1996_IGTB1996 622 0
#> IGTB2007_IGTB2007 0 1001
#> IGTB2065_IGTB2065 954 0
#>
#> $replicate_4
#>
#> A B
#> IGTB141_IGTB141 0 964
#> IGTB143_IGTB143 0 1417
#> IGTB195_IGTB195 0 1571
#> IGTB469_IGTB469 1263 0
#> IGTB498_IGTB498 0 1594
#> IGTB508_IGTB508 1317 0
#> IGTB514_IGTB514 1333 0
#> IGTB645_IGTB645 0 1299
#> IGTB670_IGTB670 0 1290
#> IGTB826_IGTB826 1154 0
#> IGTB884_IGTB884 967 0
#> IGTB986_IGTB986 926 0
#> IGTB1372_IGTB1372 836 0
#> IGTB1506_IGTB1506 0 927
#> IGTB1539_IGTB1539 818 0
#> IGTB1575_IGTB1575 0 823
#> IGTB1650_IGTB1650 1238 0
#> IGTB1762_IGTB1762 0 1815
#> IGTB1768_IGTB1768 0 1545
#> IGTB1789_IGTB1789 0 1105
#> IGTB1793_IGTB1793 0 1233
#> IGTB1814_IGTB1814 1400 0
#> IGTB1815_IGTB1815 1063 0
#> IGTB1826_IGTB1826 974 0
#> IGTB1827_IGTB1827 0 1117
#> IGTB1828_IGTB1828 0 959
#> IGTB1840_IGTB1840 1422 0
#> IGTB1844_IGTB1844 1252 0
#> IGTB1871_IGTB1871 973 0
#> IGTB1895_IGTB1895 1098 0
#> IGTB1901_IGTB1901 0 960
#> IGTB1906_IGTB1906 779 0
#> IGTB1908_IGTB1908 0 773
#> IGTB1913_IGTB1913 0 1659
#> IGTB1916_IGTB1916 0 985
#> IGTB1921_IGTB1921 0 1468
#> IGTB1952_IGTB1952 0 1263
#> IGTB1966_IGTB1966 1782 0
#> IGTB1974_IGTB1974 957 0
#> IGTB1982_IGTB1982 0 827
#> IGTB1987_IGTB1987 0 1526
#> IGTB1996_IGTB1996 622 0
#> IGTB2007_IGTB2007 1001 0
#> IGTB2065_IGTB2065 0 954
#>
#> $replicate_5
#>
#> A B
#> IGTB141_IGTB141 0 964
#> IGTB143_IGTB143 0 1417
#> IGTB195_IGTB195 1571 0
#> IGTB469_IGTB469 0 1263
#> IGTB498_IGTB498 1594 0
#> IGTB508_IGTB508 0 1317
#> IGTB514_IGTB514 1333 0
#> IGTB645_IGTB645 1299 0
#> IGTB670_IGTB670 0 1290
#> IGTB826_IGTB826 1154 0
#> IGTB884_IGTB884 967 0
#> IGTB986_IGTB986 0 926
#> IGTB1372_IGTB1372 836 0
#> IGTB1506_IGTB1506 0 927
#> IGTB1539_IGTB1539 0 818
#> IGTB1575_IGTB1575 823 0
#> IGTB1650_IGTB1650 0 1238
#> IGTB1762_IGTB1762 0 1815
#> IGTB1768_IGTB1768 0 1545
#> IGTB1789_IGTB1789 0 1105
#> IGTB1793_IGTB1793 1233 0
#> IGTB1814_IGTB1814 1400 0
#> IGTB1815_IGTB1815 1063 0
#> IGTB1826_IGTB1826 974 0
#> IGTB1827_IGTB1827 1117 0
#> IGTB1828_IGTB1828 959 0
#> IGTB1840_IGTB1840 1422 0
#> IGTB1844_IGTB1844 0 1252
#> IGTB1871_IGTB1871 973 0
#> IGTB1895_IGTB1895 1098 0
#> IGTB1901_IGTB1901 0 960
#> IGTB1906_IGTB1906 779 0
#> IGTB1908_IGTB1908 773 0
#> IGTB1913_IGTB1913 1659 0
#> IGTB1916_IGTB1916 0 985
#> IGTB1921_IGTB1921 0 1468
#> IGTB1952_IGTB1952 0 1263
#> IGTB1966_IGTB1966 0 1782
#> IGTB1974_IGTB1974 0 957
#> IGTB1982_IGTB1982 0 827
#> IGTB1987_IGTB1987 1526 0
#> IGTB1996_IGTB1996 622 0
#> IGTB2007_IGTB2007 0 1001
#> IGTB2065_IGTB2065 0 954The number of DE and non-DE genes per replicate:
map(sce_objects, ~ map_dfr(.x, ~ table(rowData(.x)$is_DE), .id = "replicate"))
#> $prop_DE_0.05
#> # A tibble: 5 Ã 3
#> replicate `FALSE` `TRUE`
#> <chr> <table> <table>
#> 1 replicate_1 4283 225
#> 2 replicate_2 4283 225
#> 3 replicate_3 4283 225
#> 4 replicate_4 4283 225
#> 5 replicate_5 4284 224t-SNE plots
t-SNE plots colored by celltype, shaped by mock_group.
tSNE_plots <- map(sce_objects, function(x) {
p <- imap(x, function(sce, name) {
plotTSNE(sce, colour_by = "ct_cov", shape_by = "mock_group") +
ggtitle(name)
})
patchwork::wrap_plots(p, ncol = 3, guides = "collect")
})Prop. DE = 0.05
Extract results of interest
Runtimes
## Nest methods below celltypes
res_list <- map(res_list, transpose)
## Get runtimes for each celltype
runtimes <- map_depth(res_list, 2,
~ map_dfr(.x, get_runtimes, depth = 1, .id = "method")
) %>%
map(bind_rows, .id = "celltype")P-values
res_tables <- map_depth(res_list, 3, get_aggregated_rep_tables, depth = 1)
res_tables <- map_depth(res_tables, 2, ~ combine_tables(.x, .id = "method"))Visualize results
Run times
runtime_plots <- imap(
runtimes,
~ plot_run_times(.x, width = 0.2, height = 0) +
ggtitle(.y) +
facet_wrap(vars(celltype))
)prop_DE_0.05
P-value distributions
pval_figs <- map_depth(res_tables, 2, ~ pval_hist(.x))prop_DE = 0.05
B_mem
#> Warning: Removed 455 rows containing non-finite values (stat_bin).
ncM
#> Warning: Removed 455 rows containing non-finite values (stat_bin).
T4_naive
#> Warning: Removed 455 rows containing non-finite values (stat_bin).
P-value distributions for non-DE genes
non_de_res <- map2(res_tables, sce_objects, function(res_by_ct, sce_by_rep) {
map(res_by_ct, function(res_table) {
res_by_rep <- split(res_table, res_table$replicate)
out <- map2(res_by_rep, sce_by_rep, function(tbl, sce) {
## Select only non-DE genes
non_de <- rownames(sce)[!rowData(sce)$is_DE]
tbl[tbl$gene %in% non_de, ]
})
bind_rows(out, .id = "replicate")
})
})non_de_pval_figs <- map_depth(non_de_res, 2, ~ pval_hist(.x))prop_DE = 0.05
B_mem
#> Warning: Removed 455 rows containing non-finite values (stat_bin).
ncM
#> Warning: Removed 455 rows containing non-finite values (stat_bin).
T4_naive
#> Warning: Removed 455 rows containing non-finite values (stat_bin).
Performance evaluation with iCOBRA
Prepare Data
P-values for missing genes are set to 1.
cobra_data <- map2(res_tables, sce_objects, function(res_by_ct, sce_by_rep) {
map(res_by_ct, function(res_table) {
res_by_rep <- split(res_table, res_table$replicate)
map2(res_by_rep, sce_by_rep, prepare_COBRAData, replace_missing = TRUE)
})
})
cobra_perf <- map_depth(cobra_data, 3, calculate_performance, binary_truth = "status")
n_methods <- length(unique(res_tables[[1]][[1]]$method)) + 1 # +1 for "truth"
cobra_objects <- map_depth(cobra_perf, 3, prepare_data_for_plot)TPR plots
no_legend <- theme(legend.position = "none")
tpr_plots <- map_depth(cobra_objects, 2, function(cobra_list) {
p <- imap(cobra_list, ~ plot_tpr(.x, title = .y) + no_legend)
patchwork::wrap_plots(p, ncol = 3)
})prop_DE = 0.05
B_mem
ncM
T4_naive
FPR plots
fpr_plots <- map_depth(cobra_objects, 2, function(cobra_list) {
p <- imap(cobra_list, ~ plot_fpr(.x, title = .y) + no_legend)
patchwork::wrap_plots(p, ncol = 3)
})prop_DE = 0.05
B_mem
ncM
T4_naive
FDR-TPR curves
fdr_tpr_plots <- map_depth(cobra_objects, 2, function(cobra_list) {
p <- imap(cobra_list, ~ plot_fdrtprcurve(.x, title = .y))
patchwork::wrap_plots(p, ncol = 3, guides = "collect")
})prop_DE = 0.05
B_mem
ncM
T4_naive
Zoomed FDR-TPR curves
Same plots but zoomed on the FDR-TPR points region.
fdr_tpr_plots_zoomed <- map_depth(cobra_objects, 2, function(cobra_list) {
p <- imap(cobra_list, ~ plot_zoomed_fdrtprcurve(.x, title = .y))
patchwork::wrap_plots(p, ncol = 3, guides = "collect")
})prop_DE = 0.05
B_mem
ncM
T4_naive
Session info
Session info
#> [1] "2022-05-30 17:25:15 CEST"
#> Local: lupus-data /Users/milan/Projects/SCandwich-analyses
#> Remote: lupus-data @ origin (git@github.com:statOmics/SCandwich-analyses.git)
#> Head: [60b481a] 2022-05-30: Small bug fix in sim-results reports
#> â Session info đŠī¸ đī¸ đ âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ
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